Apple previously released a paper titled Learning from Simulated and Unsupervised Images through Adversarial Training (https://arxiv.org/pdf/1612.07828.pdf), in which authors coined the architecture type SimGAN. As set out in the paper, SimGAN allows users to refine simulated data to make it look more realistic. In this section, we'll discuss how SimGAN architecture works.
The only thing you'll need in this section is the paper previously mentioned, which can be downloaded and read at: https://arxiv.org/pdf/1612.07828.pdf titled Learning from Simulated and Unsupervised Images through Adversarial Training.
In the SimGAN paper, authors set out to create a refiner network that can accurately improve the realism of synthetic images in an unsupervised manner. In the past, it has been quite hard to find matched simulation and real data for training such networks, but SimGAN has changed the existing landscape thanks to its focus on a simulated...
